Integrated Calendar

In real-world supervised learning problems, accurate and trustworthy labels are often elusive, with label noise being a pervasive challenge. In this talk, we will delve into the inherent robustness of conformal prediction---a powerful tool for quantifying predictive uncertainty---to label noise. We will address both regression and classification problems and characterize how and when we can generate uncertainty sets that include the true labels that are hidden from us. By navigating between theory and practice, we will showcase the conservative coverage of clean ground truth labels achieved by employing conformal prediction with noisy labels and commonly used score functions, except in adversarial cases.

Foundation models that connect vision and language have recently shown great promise for a wide array of tasks such as text-to-image generation. Significant attention has been devoted towards utilizing the visual representations learned from these powerful vision and language models. In this talk, I will present an ongoing line of research that focuses on the other direction, aiming at understanding what knowledge language models acquire through exposure to images during pretraining. We first consider in-distribution text and demonstrate how multimodally trained text encoders, such as that of CLIP, outperform models trained in a unimodal vacuum, such as BERT, over tasks that require implicit visual reasoning. Expanding to out-of-distribution text, we address a phenomenon known as sound symbolism, which studies non-trivial correlations between particular sounds and meanings across languages and demographic groups, and demonstrate the presence of this phenomenon in vision and language models such as CLIP and Stable Diffusion. Our work provides new angles for understanding what is learned by these vision and language foundation models, offering principled guidelines for designing models for tasks involving visual reasoning.

Bio:
Hadar Averbuch-Elor is an Assistant Professor at the School of Electrical Engineering in Tel Aviv University. Before that, Hadar was a postdoctoral researcher at Cornell-Tech. She completed her PhD in Electrical Engineering at Tel-Aviv University. Hadar is a recipient of several awards including the Zuckerman Postdoctoral Scholar Fellowship, the Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences, and the Alon Fellowship for the Integration of Outstanding Faculty. She was also selected as a Rising Star in EECS in 2020. Hadar's research interests lie in the intersection of computer graphics and computer vision, particularly in combining pixels with more structured modalities, such as natural language and 3D geometry.

An efficient method to inhibit pathological crystallization is the identification of modifiers, which are (macro)molecules that reduce the rate of crystal growth. Here, I will discuss progress in understanding nonclassical pathways of crystallization and the design of effective modifiers as treatments of three human diseases: kidney stones, malaria, and atherosclerosis. One of the primary tools used to explore crystal growth mechanisms and modifier-crystal interfacial interactions is in situ atomic force microscopy, which we have coupled with microfluidics to assess modifier efficacy. Results from collaborative studies with computational and medical experts have identified unique crystallization pathways, mechanisms of crystal growth inhibition, and promising new therapies, such as the discovery of hydroxycitrate as an inhibitor of calcium oxalate kidney stones. Our studies revealed that hydroxycitrate induces strain in crystals, leading to localized dissolution. A similar outcome was observed for urate stones where solute isomers function as native growth inhibitors that can induce dramatic changes in crystal morphology, and suppress crystal growth at specific conditions. I will discuss new insights into studies of kidney stone prevention and highlight their similarities and differences with novel approaches we have been developing for controlled crystallization in malaria (i.e. heme crystals) and atherosclerosis (i.e. cholesterol crystals).

Succinct non-interactive arguments (SNARGs) are a powerful
cryptographic primitive whose feasibility is still poorly understood.
However, over the last few years, a successful paradigm for building
SNARGs from standard cryptographic assumptions has emerged:
- First, build a non-interactive *batch* argument system (BARG) for NP.
- Then, use BARGs for NP to build SNARGs for various NP languages of
interest.
In this talk, we will discuss recent progress on constructing SNARGs
within this paradigm. Specifically, we study:
1) Under what computational assumptions can we build BARGs for NP?
2) For which NP languages can we build SNARGs within this paradigm?

This talk is based on joint works with Zvika Brakerski, Maya Brodsky,
Yael Kalai, Omer Paneth, Vinod Vaikuntanathan, and Daniel Wichs.

Spatial and temporal regulation of the apoptotic machinery is critical for the execution of multiple cellular events. Here we identify Seven In Absentia Homolog 3 (Siah3) as a new regulator of the cell death machinery during axonal pruning in developing mice. Sensory neurons from Siah3 KO mice exhibit delayed axonal degeneration and Caspase-3 activation in response to trophic deprivation. In agreement, the Siah3 KO mice display increased peripheral sensory innervation. Mechanistically, we show that Siah3 directly binds to the core mitophagy machinery protein Parkin, and, importantly, co-ablation of Prkn and Siah3 reverses the delay in axonal degeneration and Caspase-3 activation detected in Siah3 KO neurons. Strikingly, loss of Siah3 causes dramatic increase in axonal mitophagy upon trophic deprivation, suggesting that Siah3 is a positive regulator of axonal elimination acting by modulation of Parkin-mediated mitophagy. Overall, our results suggest that Parkin-mediated mitophagy restrains the apoptotic system by eliminating signaling mitochondria and reveal the role of mitochondrial signaling in axonal elimination.